Skip to Main Content
The appearance of micro-calcifications in mammograms is a crucial early sign of breast cancer. Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task. This paper presents novel algorithms for the detection of micro-calcifications using stochastic resonance (SR) noise. In these algorithms, a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. First, a SR noise-based detection approach is presented to improve some suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a SR noise-based detection enhancement framework is presented to deal with more general model mismatch cases. Our algorithms and the framework are tested on a set of 75 representative abnormal mammograms. They yield superior performance when compared with several classification and detection approaches developed in our work as well as those available in the literature.